 A survey on different rainfall forecasting techniquesC. Vijayalakshmi and M. Pushpa International Journal of Industrial and Systems Engineering, 2026, vol. 53, issue 1, 22-42 Abstract: Rainfall prediction is critical because it has a variety of causes, such as crop loss and property damage; rainfall forecasting is critical for increasing community resilience and welfare because it has a direct impact on agriculture. There are 50 research papers reviewed that used various rainfall forecasting techniques. The research papers are categorised and reviewed using various techniques. Among them are optimisation-based techniques, ANN-based techniques, DNN-based techniques, fuzzy-based techniques, and ML-based techniques. There is also a list of research gaps and issues identified in previous works. As a result, the researchers can implement a solution and continue their research. The works reviewed in the literature are scrutinised in terms of software tools, datasets, performance evaluation metrics, and the results obtained by those techniques. The review lists the future extent by taking into account the difficulties encountered in rainfall forecasting literary works to improve their works. Keywords: rainfall forecasting; deep learning; machine learning; deep neural network; artificial neural network; genetic algorithm; particle swarm optimisation. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:53:y:2026:i:1:p:22-42 Access Statistics for this article More articles in International Journal of Industrial and Systems Engineering from Inderscience Enterprises Ltd |
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